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Pharmacoepidemiol Drug Saf ; 33(1): e5690, 2024 Jan.
Article in English | MEDLINE | ID: mdl-37669770

ABSTRACT

PURPOSE: To evaluate the positive predictive value (PPV) of an endometrial cancer case finding algorithm using International Classification of Disease 10th revision Clinical Modification (ICD-10-CM) diagnosis codes from US insurance claims for implementation in a planned post-marketing safety study. Two algorithm variants were evaluated. METHODS: Provisional incident endometrial cancer cases were identified from 2016 through 2020 among women aged ≥50 years. One algorithm variant used diagnosis codes for malignant neoplasms of uterine sites (C54.x), excluding C54.2 (malignant neoplasm of myometrium); the other used only C54.1 (malignant neoplasm of endometrium). A random sample of medical records of recent incident provisional cases (2018-2020) was requested for adjudication. Confirmed cases showed biopsy evidence of endometrial cancer, documentation of cancer staging, or hysterectomy following diagnosis. We estimated the PPV of the variants with 95% confidence intervals (CI) excluding cases that had insufficient information. RESULTS: Of 294 provisional cases adjudicated, 85% were from outpatient settings (n = 249). Mean age at diagnosis was 69.3 years. Among the 294 adjudicated cases (identified with the broader algorithm variant), the same 223 were confirmed endometrial cancer cases by both algorithm variants. The PPV (95% CI) for the broader algorithm variant was 84.2% (79.2% and 88.3%), and for the variant using only C54.1 was 85.8% (80.9% and 89.8%). CONCLUSION: We developed and validated an algorithm using ICD-10-CM diagnosis codes to identify endometrial cancer cases in health insurance claims with a sufficiently high PPV to use in a planned post-marketing safety study.


Subject(s)
Endometrial Neoplasms , International Classification of Diseases , Humans , Female , Aged , Medical Records , Endometrial Neoplasms/diagnosis , Endometrial Neoplasms/epidemiology , Algorithms , Insurance, Health , Databases, Factual
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